Designing a Color Filter with High Overall Transmittance for Improving the Color Accuracy of Digital Cameras

Previously improved color accuracy of a given digital camera was achieved by carefully designing the spectral transmittance of a color filter to be placed in front of the camera. Specifically, the filter is designed in a way that the spectral sensitivities of the camera after filtering are approximately linearly related to the color matching functions (or tristimulus values) of the human visual system. To avoid filters that absorbed too much light, the optimization could incorporate a minimum per wavelength transmittance constraint. In this paper, we change the optimization so that the overall filter transmittance is bounded, i.e. we solve for the filter that (for a uniform white light) transmits (say) 50% of the light. Experiments demonstrate that these filters continue to solve the color correction problem (they make cameras much more colorimetric). Significantly, the optimal filters by restraining the average transmittance can deliver a further 10% improvement in terms of color accuracy compared to the prior art of bounding the low transmittance.

Effect of digitally generated colored filters on Farnsworth-Munsell 100 hue test by red-green color vision-deficient observers

In this study, the effects of four different digitally generated colored filters on the Farnsworth-Munsell 100 hue test (100-hue test) are analyzed by red-green color-vision deficient (CVD) observers. We digitally simulate the colored filters based on the spectral transmittance of four colored filters, which have been used previously. Five red-green CVD observers are subjected to the 100-hue test on a monitor under nine filter conditions, which comprise one condition without filter and eight conditions with filters. The results suggest that a colored filter that transmits long wavelengths and absorbs medium wavelengths may improve the color discrimination performance of protans and deutans.

Spectral Imaging Method for Transmissive Media

An imaging process is described which captures spectral transmittance for transmissive media. The specific application is positive and negative large-format film. The system is based on a ten channel LED backlight source and a monochrome camera. The LED source sequentially back-illuminated reference targets and film samples, with an image captured for each LED channel. From the measured data and images of reference targets, a model was developed to predict spectral transmittance. With that model, the 10 images of a sample were combined to a single 31-band spectral image. Spectral images can be used to calculate colorimetric data for each pixel. These colorimetric results show that the system produces good colorimetric predictions when compared to the most relevant FADGI guidelines. Some improvement is required for the spectral model particularly in the red region.

An enhanced quantum-inspired gravitational search algorithm for color prediction based on the absorption spectrum

Spot color is widely applied to printing and packing in modern industry, which can satisfy the individualization requirements and express the emotion of products. Color prediction is the core technique for spot color restoration. In this paper, a method that combines the least squares method and gravitation search algorithm is proposed to address the color prediction by using the absorption spectrum. Firstly, the spectral transmittance of the thin film with high transmission and low reflectance characteristics is researched to find the absorbance. Secondly, the least squares method is used to ascertain the primary colors of the spot color. Thirdly, an enhanced quantum gravitation search algorithm is designed to predict the spot color. The predicted results on the 30 spot colors show that the proposed method has higher accuracy in comparison with the three existed methods. The color differences between the prepared spot colors and the reproduced spot colors are all less than 3, in which 75% of the color differences are less 1 and 35% of the color differences are less 0.1. All the results confirm that the proposed method can predict the spot color accurately.